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Long short-term memory (LSTM) neural networks for short-termwater level prediction in Mekong river estuaries |
|---|---|
| รหัสดีโอไอ | |
| Creator | 1. Thai Thanh Tran 2. Liem Duy Nguyen 3. Pham Ngoc Hoai 4. Quoc Bao Pham 5. Phan Thi ThanhHuyen 6. Nguyen Phuong Dong 7. Ha Hoang Hieu 8. Nguyen Thu Hien |
| Title | Long short-term memory (LSTM) neural networks for short-termwater level prediction in Mekong river estuaries |
| Publisher | Research and Development Office, Prince of Songkla University |
| Publication Year | 2565 |
| Journal Title | Songklanakarin Journal of Science an Technology (SJST) |
| Journal Vol. | 44 |
| Journal No. | 4 |
| Page no. | 1057-1066 |
| Keyword | long short-term memory (LSTM), neural network, water level, Mekong river estuary |
| URL Website | https://rdo.psu.ac.th/sjst/index.php |
| ISSN | 0125-3395 |
| Abstract | This study firstly adopts a state-of-the-art deep learning approach based on a Long Short-Term Memory (LSTM) neuralnetwork for predicting the hourly water level of Mekong estuaries in Vietnam. The LSTM models were developed from around8,760 hourly data points within 2018 and were evaluated using the Nash-Sutcliffe efficiency coefficient (NSE), mean absoluteerror (MAE), and root mean square error (RMSE). The results showed that the NSE values for the training and testing steps wereboth above 0.98, which can be regarded as very good performance. Furthermore, the RMSE were between 0.09 and 0.11 m for thetraining and between 0.10 and 0.12 m for the testing, while MAE for the training ranged from 0.07 to 0.08 m and varied from 0.08to 0.10 m for the testing. The LSTM networks appear to enable high precision and robustness in water level time series prediction.The outcomes of this research have crucial implications in river water level predictions, especially from the viewpoint of employingdeep learning algorithms. |
